Resource Bank: Topical List Of Resources
Periodically Updated List Of Resources, Arranged By Topic, Data Science, Machine Learning, Statistics, Careers
Table Of Contents
1) Building A Professional Portfolio Getting A Portfolio Started
Adding & Enhancing A Portfolio
Contribute To Other Projects2) Data Science & Machine Learning Making Fictional Data
(For testing, training, demonstration)
Automating Data Collection3) Career Advice Big Career Mistakes (Big Ones)
LinkedIn4) Data Culture Data Driven Culture?
What Is A Data Set?
Columns, Variables, Dimensions . . .5) Professional Writing Resources Common Writing Mistakes
Research Questions6) Personal Meets Professional Coming Out
Building A Professional Portfolio
Building a professional portfolio takes time and dedication. These articles provide advice on how to get started. Once started, these articles also provide advice on how to keep going by adding to and enhancing your existing professional portfolio.
Adding & Enhancing A Portfolio
The article, How You Can Add To And Enhance Your Data Science Portfolio presents four specific tips (specific project ideas) that will inspire anyone working on their portfolio. Read the article for complete details, the four tips are to 1) Make a “Rosetta Stone,” 2) Make a cheat sheet, 3) Write an article about software you dislike, and 4) Contribute to someone else’s project.
How You Can Add To And Enhance Your Data Science Portfolio
A data science portfolio can be a boost to your career. A well-planned portfolio that provides value and shows your…
Contribute To Other Projects
One of the fastest ways to build and maintain skill is to contribute to open source projects. Have you not yet contributed to open source projects? This article, Beginner Friendly Data Science Projects Accepting Contributions, identifies specific projects that are beginner friendly.
Beginner Friendly Data Science Projects Accepting Contributions
Looking to contribute to an open-source software? Here are a few places to get started.
Data Science & Machine Learning
Making Fictional Data
Fictional data is handy for testing, training, and demonstration purposes.
How To Make Fictional Data
When you need data for testing, training, demonstration, or other purposes. Make your own!
Three More Ways To Make Fictional Data
An inventory tools that make fictional data
Fake Birds & Machine Learning
Using the popular bird variety data to demonstrate nearest neighbors classification
A Cookbook: Using Distance To Measure Similarity
A coding cookbook for measuring similarity with measures of distance.
Applied Distance Measures; Building Higher Education Comparison Groups
A demonstration with data from the US Department of Education, how to use distance measures to specify college…
Automating Data Collection
How To Source Federal Data: Higher Education Data
A step-by-step tutorial on how to build a reproducible longitudinal data panel from federal higher education data…
Why Do We Automate Data Collection?
The results of a point and click speed test weigh in favor of automation. How data scientists can save hundreds or…
Big Career Mistakes (Big Ones)
This article is easily one of my most read articles. It explains a mistake I made (a communication error) early in my career. This is a mistake I’ve made many times. Writing this article helped me finally find an outlook that has helped me avoid repeating this mistake. In My Biggest Career Mistake, In Data Science I explore that mistake in detail, analyze it, and offer advice on how you can avoid similar mistakes.
My Biggest Career Mistake, In Data Science
An article about the biggest mistake I made in my data science career
In Seven Paths To Data Science I write composite stories from friends, family, colleagues, and associates. These are stories about how many have found their way into fulfilling data science careers.
7 Paths To Data Science
Common, or not so common career paths Towards Data Science. How teachers, faculty, accountants, engineers, artists and…
In a “poll” I asked where data professionals look for others they will know like and trust in the field. The leading answer was LinkedIn. This article discusses that poll and its results.
Poll Results Show Why Data Scientists Need A Quality LinkedIn Profile
Job searching? Polls via LinkedIn, Twitter, & Facebook, asking data science folk where they find others in the field……
Data Driven Culture?
Discussions about “data culture” are a favorite of mine. There are as many definitions for this word as there are individuals who take part in data communities. In What Is A Data Driven Culture Anyway I define this term and also discuss how to put it into practice.
What Is A Data-Driven Culture Anyway?
Proposed: The goal of data-driven organizations is to generate new knowledge.
What Is A Data Set?
This rudimentary question isn’t a simple question. Deciding for yourself and for your team what is a data set is an important step towards establishing and maintaining a data driven culture. This article discusses my definition of what a data set is, what it is not. Read this and then use it as a discussion guide for yourself in your work on establishing a data driven culture.
What Is A Data Set?
The greatest problem with communication is the illusion it has occurred: Share what you mean when you speak about a…
Columns, Variables, Dimensions . . .
After you exploring what is a data set (previous article) review this article to go below the surface. A data set column is sometimes also called a variable, or otherwise sometimes also a dimension. This article, A Closer Look AT Data Set Columns explores how and why these words often mean the same things, but sometimes not.
A Closer Look At Dataset Columns
A look at a dataset’s columns and other related key terms
Professional Writing Resources
Common Writing Mistakes
As the title explains, “Nobody is Perfect.” If you’re looking for living proof of this, find my records online. You’ll find mistake after mistake (so many mistakes). Nobody is Perfect: Words & Phrases Data Scientists Or Other Scientists Sometimes Misuse discusses words and phrases that many sometimes misuse in their writing.
Nobody is Perfect: Words & Phrases Data Scientists Or Other Scientists Sometimes Misuse
A few tips for researchers, scientists, faculty, students, or others as you write and edit.
Often, good writing starts with having asked and answered a meaningful question. In the article Asking Research Questions That Matter I provide guidance on selecting quality research questions.
Asking Research Questions That Matter
The art of asking worthwhile research questions.
The ProWritingAid.com Plunge
This article on ProWritingAid.com which contains affiliate marketing links, describes some of the reasons I adopted ProWritingAid.com to help me be a better writer.
A Data Scientist Takes the ProWritingAid Plunge
This one feature persuaded me to join the ProWritingAid club (Markdown). Must-have tool for data scientists looking to…
Personal Meets Professional
In the late 1990s I came out of the closet. In the last 22+ years since coming out I still look back at the lessons I learned on that occasion for guidance and wisdom. This article Quality of Life: Happy Anniversary originally appeared in magazine known as Our Lives.
Quality of Life: Happy Anniversary
Making the choice to come out let Adam Ross Nelson live openly. 10 years later he looks back at how he did it.
Thanks For Reading
If you like what I have to say, find more at: adamrossnelson.medium.com.
Thanks for reading. Send me your thoughts and ideas. You can write just to say hey. And if you really need to tell me how I got it wrong, I look forward to chatting soon. Twitter: @adamrossnelson | LinkedIn: Adam Ross Nelson| Facebook: Adam Ross Nelson.